Sybil：一种经过验证的深度学习模型，一次低剂量胸部计算机断层扫描预测未来肺癌风险的低剂量胸部计算机断层扫描

作者：SCI天天读

SCI 3 February 2023

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

（J Clin Oncol IF: 44.54; )

CORRESPONDING AUTHOR :Lecia V. Sequist, MD, MPH, Department of Medicine, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114; e-mail: lvsequist@partners.org.

PURPOSE 目的

Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data.

用于肺癌筛查的低剂量计算机断层扫描（LDCT）是有效的，尽管大多数符合条件的人都没有被筛查。提供个性化的未来癌症风险评估的工具可以将方法集中于最有可能受益的人。我们假设可以建立一个评估整个LDCT数据的深度学习模型来预测个人风险，而不需要额外的人口统计或临床数据。

METHODS 方法

We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers).

展开全文

我们利用国家肺部筛查试验（NLST）的LDCTs开发了一个名为Sybil的模型。Sybil只需要一个LDCT，不需要临床数据或放射科医生的注释；它可以在放射科阅读站的后台实时运行。Sybil在三个独立的数据集上进行了验证：一个由NLST参与者的6,282张LDCTs组成的保留数据集，麻省总医院（MGH）的8,821张LDCTs，以及长庚纪念医院（CGMH，包括有各种吸烟史的人，包括不吸烟者）的12,280张LDCTs。

RESULTS 结果

Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively.

Sybil对NLST的肺癌预测在1年内的接受者操作曲线下面积为0.92（95%CI，0.88至0.95），对MGH为0.86（95%CI，0.82至0.90），对CGMH外部验证集为0.94（95%CI，0.91至1.00）。6年内，NLST、MGH和CGMH的一致性指数分别为0.75（95%CI，0.72至0.78）、0.81（95%CI，0.77至0.85）和0.80（95%CI，0.75至0.86）。

CONCLUSION 结论

Sybil can accurately predict an individual’s future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil’s clinical applications. Our model and annotations are publicly available.

Sybil可以通过一次LDCT扫描准确预测个人未来的肺癌风险，进一步实现个性化筛查。要了解Sybil的临床应用，还需要进行未来的研究。我们的模型和注释是公开可用的。

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